Input uncertainty propagation methods and hazard mapping of geophysical mass flows

This paper presents several standard and new methods for characterizing the effect of input data uncertainty on model output for hazardous geophysical mass flows. Note that we do not attempt here to characterize the inherent randomness of such flow events. We focus here on the problem of characteriz...

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Bibliographic Details
Published in:Journal of Geophysical Research - Solid Earth 2008-05, Vol.113 (B5), p.B05203-n/a
Main Authors: Dalbey, K., Patra, A. K., Pitman, E. B., Bursik, M. I., Sheridan, M. F.
Format: Article
Language:English
Subjects:
computational fluid dynamics
Computational Geophysics
Earth sciences
Earth, ocean, space
Eruption mechanisms and flow emplacement
Exact sciences and technology
geophysical flow
Mathematical Geophysics
Numerical solutions
uncertainty
Uncertainty quantification
Volcanic hazards and risks
Volcanology
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Summary:This paper presents several standard and new methods for characterizing the effect of input data uncertainty on model output for hazardous geophysical mass flows. Note that we do not attempt here to characterize the inherent randomness of such flow events. We focus here on the problem of characterizing uncertainty in model output due to lack of knowledge of such input for a particular event. Methods applied include classical Monte Carlo and Latin hypercube sampling and more recent stochastic collocation, polynomial chaos, spectral projection and a newly developed extension thereof named polynomial chaos quadrature. The simple and robust samplings based Monte Carlo type methods are usually computationally intractable for reasonable physical models, while the more sophisticated and computationally efficient polynomial chaos method often breaks down for complex models. The spectral projection and polynomial chaos quadrature methods discussed here produce results of quality comparable to the polynomial chaos type methods while preserving the simplicity and robustness of the Monte Carlo‐type sampling based approaches at much lower cost. The computational efficiency, however, degrades with increasing numbers of random variables. A procedure for converting the output uncertainty characterization into a map showing the probability of a hazard threshold being exceeded is also presented. The uncertainty quantification procedures are applied first in simple settings to illustrate the procedure and then subsequently applied to the 1991 block‐and‐ash flows at Colima Volcano, Mexico.
ISSN:0148-0227
2156-2202
DOI:10.1029/2006JB004471